Fazhi Song

TL;DR: A novel iterative learning identification method that utilizes the partial but most pertinent information in the error signal is proposed to identify the force ripple in permanent-magnet linear synchronous motor (PMLSM) systems.

TL;DR: A novel data-driven feedforward tuning method that utilizes error data from all past iterations via an integrator in the learning law, yet without the need of the plant model or the sensitivity function is developed in the presence of noise.

TL;DR: In this paper, a variable-gain iterative feedforward tuning (VGIFFT) method is proposed to achieve high performance regardless of reference variations through feedforward parameterization and especially high robustness against stochastic disturbance and model uncertainty through a variable learning gain.

TL;DR: A new iterative learning control (ILC) is proposed, which embeds the reference model rather than the plant model to achieve a good transient performance and a fast convergence and is applied to the precision control of a wafer stage.

TL;DR: In this article , a cross-coupling iterative learning control (ILC) with two inputs and two outputs is proposed and then decomposed into two ILC with the same convergence condition, a master-slave ILC for the reticle stage and an independent learning control for the wafer stage.